만성췌장염은 췌장암 발생의 위험인자 중 하나로, 췌장암 감시가 주목을 받고 있다. 유전자 변이 여부에 따라 유전성 만성췌장염과 산발성 만성췌장염으로 분류할 수 있다. PRSS1 변이 유전성 만성췌장염의 경우 췌장암 발생 위험이 매우 높아 40세 이후 선별 검사 및 추적 감시가 필요하다. 그 외의 유전성 만성췌장염 및 산발성 만성췌장염에서 췌장암의 발생률은 상대적으로 낮아 선별 검사 및 추적 감시를 권고하지 않는다. 췌장암 선별검사의 방법으로 복부컴퓨터단층촬영 또는 췌장자기공명영상이 적합하다. 내시경초음파는 췌장실질의 염증, 섬유화 및 석회화로 인하여 권유하지 않는다. 산발성 만성췌장염에서도 다양한 췌장암 위험인자가 동반되는 경우 췌장암의 발생 빈도가 상당히 증가하고 만성췌장염 진단 후 5년 동안은 췌장암 발생률이 지속적으로 높아 환자의 증상 및 개별적 상황에 따른 췌장암 선별 검사 시행 및 추적 감시를 고려할 수 있다. 만성췌장염은 영양실조 발생이 높으므로 관심, 영양상태 평가 및 적극적인 영양요법이 필요하다. 필수 영양소 공급뿐만 아니라 미량영양소 공급을 놓치지 않도록 한다. 그리고 만성췌장염 환자에서 골다공증 또는 골감소증의 위험이 높음을 인지하고 이의 진단, 예방 및 치료에 관심을 둘 필요가 있다. 각 기관의 영양지원팀에 의뢰하여 적극적으로 영양요법을 시행하는 것도 도움이 되겠다.

The integration of machine learning for species identification is becoming increasingly important in entomological research. However, automatic species identification faces significant challenges such as low resolution, sample discoration, and small dataset sizes, which impede the reliability of traditional machine learning methods. Building upon the previous research on quantification of the color patterns of Stenaptinus occipitalis jessoensis using R-based analysis, this study demonstrates how to overcome these challenges in training machine learning for species identification. This approach allowed us to successfully classify geographic variations of S. occipitalis. Our results demonstrate the model's ability to identify these variations, despite the small size of the image datasets. This advancement shed some light on the potential of machine learning to identify morphological variation in highly polymorphic species.

식물기생선충은 식물에 기생하여 식물의 생장을 저하시키거나 심할 경우 식물의 고사를 일으키는 주요 병원 체의 하나이다. 이들 선충은 수입 식물을 통해 국내로 유입될 수 있으며, 이로 인해 식물의 생장이 저하되거나 식물이 죽는 등의 실제적 피해를 야기할 수 있다. 생강은 한국의 대표 음식인 김치를 비롯한 다양한 요리와 음료, 전통약재 등에 사용되는 중요한 식약재료이며, 그 가치와 수요가 매우 높다. 최근 10년(2014-2023년) 동안 종구로 써 사용되는 재식용 생강의 수입량은 총 31,740톤이었는데, 수입 생강 원산지의 대부분은 중국으로써 최근 10년 간 수입량의 99.99%를 차지했다. 동기간 검역처분 내역은 합격 수량이 1,090건(27,637톤), 폐기 수량은 222건 (4,102톤)이었다. 폐기 처분을 받는 주용 원인을 분석해보면, 관리급 선충 검출이 139건, 종구에 흙이 부착된 경우 가 26건 등이었다. 앞으로도 농림축산검역본부는 수입 재식용 생강에 대한 실험실정밀검역을 통해 우리나라의 농업과 자연환경에 위해를 가할 수 있는 식물기생선충의 차단을 위해 최선의 노력을 다하고자 한다.

The current inspection count for imported grains is 37,072. The scientific management of stored grain, which includes methodical pest identification and control procedures, is highly prioritized in the nations that export these grains. International documents on stored grain pests include a thorough description of all life phases, including mites and larvae, as well as methodical treatment techniques. They are more valuable than domestic manuals because of their comprehensive coverage and methodical management strategies. There is lack of genetic resources and photographs since the identification of stored grain pests in the domestic have been based on data from before 2017. During the course of 13 years(2010-2022) 1,469 incidences of stored grain pests were detected. Of these, 7 orders 34 families and 81 species had cases where the identification was confirmed down to the species level, for a total of 963 cases. This number shows that about 18% of the domestic quarantine site’s stored grain pests are not species-identified. Objectives in this study are to present genetic barcode data, high-resolution photoes for classification and identification, and information on international stored grain pest management techniques. Building on this, a new identification manuals for stored grain pests might be created, which would improve the site’s taxonomic identification levels.

Pyrethroid resistance in cockroach populations has been a public health challenge since the 1950s. The pyrethroid resistance in the German cockroach, Blattella germanica, is primarily attributed to knockdown resistance (kdr) mutations (E434K, C764R, and L993F) in the voltage-sensitive sodium channel gene (vssc). In this study, the pyrethroid resistance state of the German cockroach in the Republic of Korea (ROK) was assessed by analyzing the frequencies of kdr mutations using one-step PCR with total RNA. The results revealed that among the 25 populations examined, 14 populations exhibited the L993F kdr mutation, while no other mutations were detected. Since other cockroach species are also commonly found in human dwellings in ROK, the vssc genes were cloned from four other species, including Blattella nipponica, Periplaneta americana, Periplaneta japonica, and Periplaneta fuliginosa. Based on the genomic DNA (gDNA) sequences obtained from the vssc cloning, primer sets were designed to amplify the vssc fragment spanning the L993F mutation for each species and used to monitor the development of pyrethroid resistance in cockroach populations in the ROK. The study will facilitate the implementation of a nationwide monitoring program to assess cockroach resistance and select suitable alternatives.

Scrub typhus is a zoonotic bacterial disease caused by Orientia tsutsugamushi (Rickettsiales: Rickettsiaceae) and trombiculid mite larvae, also known as chigger, are known vector. Until recently, O. tsutsugamushi is the only species of the genus. However, two new species Candidatus O. chuto and Candidatus O. chiloensis that causes scrub typhus were reported recently in the Middle East, southern Chile, and Africa. In addition, Orientia spp. bacteria detected from field collected free-living Eutrombicula chigger mites in the United States. Despite these trends, research on new species is insufficient in the Republic of Korea. Therefore, we focused on finding the presence of novel species or strains from chiggers. Specimen chiggers harvested from rodents collected in 16 regions in October 2022 collaborating with the Regional Center for Vector Surveillance against Climate Change were selected. A total of 1,249 specimen belonging 4 genera and 14 species were identified by using a fluorescence microscope and 266 pools were produced by pooling up to 10 individuals per species chiggers. To detect Orientia spp., we screened by using a real-time PCR targeting the 16S rRNA gene. Overall minimum infection rate was 0.56% (7 pools/1,249 tested specimen). After screening, conventional nested-PCR for targeting 47-kDa htrA gene was conducted to obtain sequences, and four of the positive pools were amplified. Through phylogenetic analysis, three pools were clustered with O. tsutsugamushi Gilliam and UT221 strain, and the other pool was formed a clade that was distinct from O. tsutsugamushi. These results suggest that novel species of Orientia may exist in the Republic of Korea.

최근 광주 화정아이파크, 인천 검단 신도시 아파트 사고 등 국내에서 건축물 안전사고가 잇따라 발 생하고 있다. 시공 중 발생한 구조물 붕괴로 인해 인명·재산 피해가 수반된 대형 건설사고가 다수 발 생하였다. 건축물 안전사고의 발생 원인으로 무단 구조변경, 설계 및 시공 시 철근 누락 등이 제시되 면서 부실 감리에 대한 우려가 증가하고 있다. 하지만 현실적으로 건설 현장의 모든 장소에서 감리직 원이 상주하며 확인하는 것은 불가능하며 시공 정확도 검사 역시 감리자의 경험에 근거하여 육안 판 독 및 일부 수작업 계측으로 진행되고 있다. 감리 작업의 효율성을 높이기 위해 최근에는 3D 스캐너, Depth Camera 등을 구조 감리 기법 연구가 진행되고 있다. 철근 길이와 철근 배근 간격에 대한 연구 는 많이 진행되었지만 철근 직경의 검출 정확도는 아직 미흡한 상황이며, 특히 직경이 작은 D10과 D13의 구별에서는 한계를 나타내고 있다. 따라서 본 연구에서는 접근성이 용이한 스마트폰을 사용하 여 영상을 획득하고 이를 기반으로 3D 포인트 클라우드를 제가한 다음 철근 직경, 철근 길이, 철근 배근 간격 등의 자동 검측 기술을 개발하고 건설현장에서의 적용 가능성을 확인하고자 한다. 검증을 위한 실험체는 길이 2100mm, 폭 195mm, 높이 395mm의 철근 조립 상태의 보이다. 포인트 클라우드 제작을 위한 영상 촬영은 iPhone SE (3rd generation)을 사용하였다. 이후 MATLAB과 METASHAPE 를 사용하여 포인트 클라우드를 생성하고 Computer Vision과 Image Processing 기술을 활용하여 구 하고자 하는 철근 정보를 자동 검출하였다. 이후 실제 측정한 값과 자동 검출한 값을 비교하여 개발한 기법에 대한 적합성을 확인하였다.

Deep learning-based computer vision anomaly detection algorithms are widely utilized in various fields. Especially in the manufacturing industry, the difficulty in collecting abnormal data compared to normal data, and the challenge of defining all potential abnormalities in advance, have led to an increasing demand for unsupervised learning methods that rely on normal data. In this study, we conducted a comparative analysis of deep learning-based unsupervised learning algorithms that define and detect abnormalities that can occur when transparent contact lenses are immersed in liquid solution. We validated and applied the unsupervised learning algorithms used in this study to the existing anomaly detection benchmark dataset, MvTecAD. The existing anomaly detection benchmark dataset primarily consists of solid objects, whereas in our study, we compared unsupervised learning-based algorithms in experiments judging the shape and presence of lenses submerged in liquid. Among the algorithms analyzed, EfficientAD showed an AUROC and F1-score of 0.97 in image-level tests. However, the F1-score decreased to 0.18 in pixel-level tests, making it challenging to determine the locations where abnormalities occurred. Despite this, EfficientAD demonstrated excellent performance in image-level tests classifying normal and abnormal instances, suggesting that with the collection and training of large-scale data in real industrial settings, it is expected to exhibit even better performance.

This work involves the development of a novel waste-derived carbon dots (CDs) conjugated with silver (Ag) nanohybrid system-based Fluorescence Resonance Energy Transfer (FRET) sensor for the detection of melamine. CDs and Ag nanoparticles served as energy donors and energy acceptors, respectively. CDs were synthesized from orange peel waste through a combined hydrothermal and ultra-sonication route. The synthesized CDs had hydroxyl, amino, and carboxyl groups on their surface, explaining that waste-derived CDs can act as reducing and stabilizing agents and showed strong absorption and fluorescence emission at 305 and 460 nm, respectively. The bandgap, linear refractive index, conduction band, and valance band potential of CDs were observed to be 2.86, 1.849, 1.14, and 4.002 eV, respectively. No significant difference was observed in the fluorescence properties at different pH (acid and alkaline) and ionic concentrations. Given their fluorescent nature, the synthesized CDs were used for the detection of melamine. The fluorescence of CDs was found to be quenched by Ag+ due to the FRET energy transfer between CDs to Ag. Notably, the zeta potential of Ag@CDs was changed from − 28.7 mV to − 30.6 mV after the incorporation of Ag+. Ag@CDs showed excellent selectivity and sensitivity toward the sensing of melamine in the aqueous solutions with the limit of detection ~ 0.85 μM. Increasing the melamine level also raises the FL intensity of Ag@CDs. The substrate was effectively used in the detection of melamine in milk as a real application and the recovery percentage was found to be 98.03%. Moreover, other adulterants such as urea and formaldehyde can be detected selectively by Ag@CDs. Overall, the synthesized Ag@CDs can be used as an efficient material for sensing applications involving such food adulterants.

Black phosphorus (BP) is incorporated in the electrochemical detection of uric acid (UA) to form few layers of BP nanosheets (BPNS)-modified glassy carbon electrodes (BPNS/GCE), investigated by means of ultrasound-assisted liquid-phase exfoliation. We find a significant increase in the peak current magnitude and positive potential shift in the electrochemical response of BPNS/GCE, which may be attributed to the larger specific surface area and good charge transfer ability of BPNS. Further, the electrochemical response of BPNS/GCE is evaluated under different conditions to achieve the optimal conditions. UA detection using differential pulse voltammetry (DPV) shows linear response within the range of 1–1000 μM with a detection limit of 0.33 μM. This work reveals new applications of BP nanomaterials in the electrochemical sensing, thereby promoting further advancement in terms of practical applications of two-dimensional nanomaterials.

This study presents the fabrication and application of a graphene-assisted voltammetry platform for the sensitive detection of nitrate ions in PM2.5 (atmospheric aerosols with a maximum diameter of 2.5 μm). The MoS2/ reduced graphene oxide/ glassy carbon electrode ( MoS2/rGO/GCE) was prepared using a simple and efficient electrochemical deposition method. The rationale behind selecting MoS2/ rGO stems from their individual properties that, when combined, can enhance the electrode’s performance. MoS2 offers excellent electro-catalytic activity and selectivity for nitrate ion detection, while rGO provides high conductivity and a large surface area for enhanced sensitivity. The electrochemical performance of MoS2/ rGO/GCE was investigated and compared with MoS2/ GCE and bare GCE using cyclic voltammetry and electrochemical impedance spectroscopy. The results demonstrated that MoS2/ rGO/GCE exhibited enhanced electro-catalytic activity, high conductivity, and improved selectivity for nitrate ion detection. The optimal pH value for detecting nitrate ions was determined to be 8.0. Differential pulse voltammetry (DPV) was employed to investigate the linear range and detection limit of nitrate ions on MoS2/ rGO/GCE, resulting in a linear range from 1 to 300 μM and a detection limit of 0.35 μM. The reproducibility and the stability of MoS2/ rGO/GCE were assessed, showing satisfactory performance. Real sample analysis from Chengdu City showed a strong correlation between the results obtained using MoS2/ rGO/GCE and ion chromatography, highlighting its potential application in monitoring nitrate ions in PM2.5. The findings of this study contribute to the development of a graphene-assisted voltammetry platform for sensitive nitrate ion detection in PM2.5, offering potential benefits for real-time air pollution monitoring and environmental health assessments.

In the present study, an innovative electrochemical sensing platform was established for sensitive detection of NO2 —. This sensor was developed using CoFe alloy encapsulated in nitrogen-doped carbon nanocubes (named as CoFe@NC-NCS), synthesized through the calcination of polydopamine-coated CoFe Prussian-blue analogues (CoFe-PBA@PDA). The morphological and electrochemical characterization reveals that the CoFe@NC-NCS possesses high electrocatalytic activity for electrochemical quantitation of NO2 —, ascribed to the huge surface area and plentiful active positions, benefiting from the porous, hollow, and core–shell structure of CoFe@NC-NCS. Under the optimal conditions, CoFe@NC-NCS/GCE possessed remarkable sensing performance for NO2 — with wide liner ranges and a detection limit of 0.015 μM. NO2 — recovery experiments in real samples exhibited recoveries in the range of 98.8–103.5%. Hence, the CoFe@NC-NCS shows great promise for the construction of electrochemical sensor with more potential application.

In this research, reduced graphene oxide/polypyrrole (rGO/PPy) particles were synthesized and used to measure the amount of dopamine (DA) electrochemically. The obtained rGO/PPy particle was characterized by Fourier Transform Infrared Spectrophotometer (FTIR), UV–Visible Spectrophotometer (UV–Vis), and X-Ray Diffraction Diffractometry (XRD). To investigate the DA sensor performance, cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used to acquire electrochemical measurements of the sensor. Current values of 1.65 and 5.9 mA were observed in the CV at 0.2 mM and 1.2 mM concentrations of target molecule, respectively. Under optimized conditions, the linear calibration plots were found to exhibit significant sensitivity in the linear range of 0.2 and 1.2 mM, with a corresponding detection limit of 0.061 μM for DA. The results obtained were similar to the sensor results of DA made using precious metals. This work was a demonstration of the feasibility of high-sensitivity electrochemical analysis with conductive carbon materials without the use of precious metals. It was also observed that the cost-effective rGO/PPy exhibited a very high potential for DA detection.

Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.

Background: Single nucleotide polymorphisms (SNPs) are widely used genetic markers with applications in human disease diagnostics, animal breeding, and evolutionary studies, but existing genotyping methods can be labor-intensive and costly. The aim of this study is to develop a simple and rapid method for identification of a single nucleotide change. Methods: A modified Polymerase Chain Reaction Amplification of Multiple Specific Alleles (PAMSA) and high resolution melt (HRM) analysis was performed to discriminate a bovine polymorphism in the NCAPG gene (rs109570900, 1326T > G). Results: The inclusion of tails in the primers enabled allele discrimination based on PCR product lengths, detected through agarose gel electrophoresis, successfully determining various genotypes, albeit with some time and labor intensity due to the use of relatively costly high-resolution agarose gels. Additionally, high-resolution melt (HRM) analysis with tailed primers effectively distinguished the GG genotype from the TT genotype in bovine muscle cell lines, offering a reliable way to distinguish SNP polymorphisms without the need for time-consuming AS-PCR. Conclusions: Our experiments demonstrated the importance of incorporating unique mismatched bases in the allele-specific primers to prevent cross-amplification by fragmented primers. This efficient and cost-effective method, as presented here, enables genotyping laboratories to analyze SNPs using standard real-time PCR.

Smart factory companies are installing various sensors in production facilities and collecting field data. However, there are relatively few companies that actively utilize collected data, academic research using field data is actively underway. This study seeks to develop a model that detects anomalies in the process by analyzing spindle power data from a company that processes shafts used in automobile throttle valves. Since the data collected during machining processing is time series data, the model was developed through unsupervised learning by applying the Holt Winters technique and various deep learning algorithms such as RNN, LSTM, GRU, BiRNN, BiLSTM, and BiGRU. To evaluate each model, the difference between predicted and actual values was compared using MSE and RMSE. The BiLSTM model showed the optimal results based on RMSE. In order to diagnose abnormalities in the developed model, the critical point was set using statistical techniques in consultation with experts in the field and verified. By collecting and preprocessing real-world data and developing a model, this study serves as a case study of utilizing time-series data in small and medium-sized enterprises.

본 논문에서는 대규모 실시간 매칭의 생존 게임에서 플레이를 위한 유저들의 소셜 관계에 대해 연구한다. 특 히 “사전 팀 구성”을 통한 자의적인 팀 구성이 어떤 방식으로 유저들을 연결하는 지 연구하고자 한다. 다수 의 사람 간 집단 역학에서 나타나는 특성이나 패턴에 대한 조사를 중심으로 하였으며, 개인의 특성은 보조적 인 수단으로만 사용된다. 이번 연구에서는 게임을 플레이하는 유저들의 익명화 된 대규모 데이터를 활용하며 이에 대한 간소화된 집계 방법을 제안한다. 데이터 세트에는 사전 팀 구성에 관한 11,259만 줄의 속성이 포 함되어 있으며, 데이터에서 우리는 250만개의 노드와 1,182만개의 무방향 에지가 있는 협업 네트워크를 구성 하여 대규모 게임 내 협동 네트워크를 만듭니다. 연결 정도, 경로 길이, 클러스터링 및 소속 하위 컴포넌트의 크기 등 네트워크에 관한 수치를 통해 게임내 소셜 활동에 대한 이해를 높이고자 한다. 본 논문에서는 다음 의 두가지 특성을 중심으로 결론을 제시한다. 첫째, 네트워크 내에는 대규모로 연결된 2개(전체의 44% 및 2%)와 나머지의 파편화된 하위 컴포넌트로 구성 되어있다. 이 대규모 컴포넌트 중 작은 쪽은 한국 유저로만 구성되어 있다. 둘째, 컴포넌트 크기 별 평균 연결 거리와 군집화 계수, k-core를 확인함으로써 기타 다른 네 트워크 대비 이웃 간 연결이 강하면서 전체적으로는 비교적 멀리 떨어져 있음을 확인한다.

본 연구는 네트워크 이상 감지 및 예측을 위해 벡터 자기회귀(VAR) 모델의 사용을 비교 분석한다. VAR 모 델에 대한 간략한 개요를 제공하고 네트워크 이상 체크로 사용 가능한 두 가지 버전을 검토하며 두 종류의 VAR 모델을 통한 경험적인 평가를 제시한다. VAR-Filtered moving-common-AR 모델이 단일 노드 이상 감지 성능에서 우수하며, VAR-Adaptive Learning 버전은 몇 개의 노드 간 이상을 효과적으로 식별하는 데 특히 효 과적이며 두 가지 주요VAR 모델의 전반적인 성능 차이에 대한 근본적인 이유도 분석한다. 각 기술의 장단점 을 개요로 제공하고 성능 향상을 위한 제안도 제시하고자 한다.

Efficiently detecting the nearest navigational dangers in Electronic Chart Display and Information Systems (ECDIS) remains pivotal for maritime safety. However, the software implementation of ADMAR(Automatic Distance measurement and Ranging) functionality faced challenges, necessitating extensive computations across ENC cells and impacting real-time performance. To address this, we present a novel method employing dynamic programming. Our proposed algorithm strategically organizes nodes into a tree structure, optimizing the search process towards nodes likely to contain navigational hazards. Implementation of this method resulted in a notable sevenfold reduction in computation time compared to the conventional Brute Force approach. Our study established a direct correlation between the ADMAR functionality and node count, achieving error margins deemed acceptable for practical navigation scenarios. Despite this theoretical progress, minor errors in results prompt further refinement. Consequently, future iterations will explore varying values for N, considering hierarchy and cell sizes to enhance algorithmic precision. This research signifies a potential advancement in optimizing navigational danger detection within ECDIS, offering a promising avenue for improved efficiency. By introducing a dynamic programming-based approach, we have streamlined the detection process while acknowledging the scope for algorithmic refinement in subsequent studies. Our findings underline the feasibility of employing dynamic programming to enhance navigational danger detection, emphasizing its potential in ensuring maritime safety. This work lays a foundation for future research endeavors, aiming to fine-tune algorithms and advance navigational safety measures in ECDIS.

해상교통관제센터(VTS)의 관제사는 구역 내 교통 상황을 빠르고 정확하게 파악하여 관제가 필요한 선박에게 정보를 제공하는 역할을 수행한다. 그러나 교통량이 급격히 증가하는 경우 관제사의 업무 부하로 인해 관제 공백이 발생하기도 한다. 이러한 이유에서 관 제사의 업무 부하를 줄이고, 일관성 있는 관제 정보를 제공할 수 있는 관제 지원 기술의 개발이 필요한 실정이며, 본 논문에서는 구역 내 이상 운항 선박을 자동으로 식별하는 모델을 제안하였다. 제안하는 이상 운항 식별 모델은 규칙 기반 모델, 위치 기반 모델, 맥락 기반 모 델로 구성되며, 대상 해역의 교통 특성에 최적화된 교통 네트워크 모델을 사용하는 특징이 있다. 구현된 모델은 시범센터(대산항 VTS)에 서 수집되는 실해역 데이터를 적용하여 실험을 수행하였다. 실험을 통해 실해역의 다양한 이상 운항 상황이 자동으로 식별됨을 확인하였 고, 전문가 평가를 통해 식별 결과를 검증하였다.